Image marking with error correction

ABSTRACT

A method of marking a work of authorship without apparent evidence of data alteration. The work (e.g., an image) is represented by a set of data elements (e.g., pixels). The marking includes providing first plural-bit data, and computing therefrom additional, error correcting data. This composite set of data is used in image marking, permitting at least certain errors to be discerned and corrected when the marking is later discerned from the image.

TECHNICAL FIELD

This application is a continuation of application Ser. No. 09/317,784,filed May 24, 1999, now U.S. Pat. No. 6,072,888, application Ser. No.09/074,632, filed May 7, 1998, now U.S. Pat. No. 5,930,377, which is acontinuation of application Ser. No. 08/969,072, filed Nov. 12, 1997,now U.S. Pat. No. 5,809,160, which is a continuation of application Ser.No. 07/923,841, filed Jul. 31, 1992, now U.S. Pat. No. 5,721,788.

BACKGROUND OF THE INVENTION

Various images in traditional print or photographic media are commonlydistributed to many users. Examples include the distribution of printsof paintings to the general public and photographs and film clips to andamong the media. Owners may wish to audit usage of their images in printand electronic media, and so require a method to analyze print, film anddigital images to determine if they were obtained directly from theowners or derived from their images. For example, the owner of an imagemay desire to limit access or use of the image. To monitor and enforcesuch a limitation, it would be beneficial to have a method of verifyingthat a subject image is copied or derived from the owner's image. Themethod of proof should be accurate and incapable of being circumvented.Further, the method should be able to detect unauthorized copies thathave been resized, rotated, cropped, or otherwise altered slightly.

In the computer field, digital signatures have been applied to non-imagedigital data in order to identify the origin of the data. For variousreasons these prior art digital signatures have not been applied todigital image data. One reason is that these prior art digitalsignatures are lost if the data to which they are applied are modified.Digital images are often modified each time they are printed, scanned,copied, or photographed due to unintentional “noise” created by themechanical reproduction equipment used. Further, it is often desired toresize, rotate, crop or otherwise intentionally modify the image.Accordingly, the existing digital signatures are unacceptable for usewith digital images.

SUMMARY OF THE INVENTION

The invention includes a method and system for embedding imagesignatures within visual images, applicable in the preferred embodimentsdescribed herein to digital representations as well as other media suchas print or film. The signatures identify the source or ownership ofimages and distinguish between different copies of a single image. Inpreferred embodiments, these signatures persist through image transformssuch as resizing and conversion to or from print or film and so providea method to track subsequent use of digital images including derivativeimages in print or other form.

In a preferred embodiment described herein, a plurality of signaturepoints are selected that are positioned within an original image havingpixels with pixel values. The pixel values of the signatures points areadjusted by an amount detectable by a digital scanner. The adjustedsignature points form a digital signature that is stored for futureidentification of subject images derived from the image.

The preferred embodiment of the invention described herein embeds asignature within the original image by locating candidate points such asrelative extrema in the pixel values. Signature points are selected fromamong the candidate points and a data bit is encoded at each signaturepoint by adjusting the pixel value at and surrounding each point.Preferably, the signature is redundantly embedded in the image such thatany of the redundant representations can be used to identify thesignature. The signature is stored for later use in identifying asubject image.

According to a preferred embodiment, the identification of a subjectimage includes ensuring that the subject image is normalized, i.e., ofthe same size, rotation, and brightness level as the original image. Ifnot already normalized, the subject image is normalized by aligning andadjusting the luminance values of subsets of the pixels in the subjectimage to match corresponding subsets in the original image. Thenormalized subject image is then subtracted from the original image andthe result is compared with the stored digital signature. In analternate embodiment, the normalized subject image is compared directlywith the signed image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a computer system used in a preferred embodimentof the present invention.

FIG. 2 is a sample digital image upon which a preferred embodiment ofthe present invention is employed.

FIG. 3 is a representation of a digital image in the form of an array ofpixels with pixel values.

FIG. 4 is a graphical representation of pixel values showing relativeminima and maxima pixel values.

FIG. 5 is a digital subject image that is compared to the image of FIG.2 according to a preferred embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention includes a method and system for embedding asignature into an original image to create a signed image. A preferredembodiment includes selecting a large number of candidate points in theoriginal image and selecting a number of signature points from among thecandidate points. The signature points are altered slightly to form thesignature. The signature points are stored for later use in auditing asubject image to determine whether the subject image is derived from thesigned image.

The signatures are encoded in the visible domain of the image and sobecome part of the image and cannot be detected or removed without priorknowledge of the signature. A key point is that while the changesmanifested by the signature are too slight to be visible to the humaneye, they are easily and consistently recognizable by a common digitalimage scanner, after which the signature is extracted, interpreted andverified by a software algorithm.

In contrast to prior art signature methods used on non-image data, thesignatures persist through significant image transformations thatpreserve the visible image but may completely change the digital data.The specific transforms allowed include resizing the image larger orsmaller, rotating the image, uniformly adjusting color, brightnessand/or contrast, and limited cropping. Significantly, the signaturespersist through the process of printing the image to paper or film andrescanning it into digital form.

Shown in FIG. 1 is a computer system 10 that is used to carry out anembodiment of the present invention. The computer system 10 includes acomputer 12 having the usual complement of memory and logic circuits, adisplay monitor 14, a keyboard 16, and a mouse 18 or other pointingdevice. The computer system also includes a digital scanner 20 that isused to create a digital image representative of an original image suchas photograph or painting. Typically, delicate images, such aspaintings, are converted to print or film before being scanned intodigital form. In one embodiment a printer 22 is connected to thecomputer 12 to print digital images output from the processor. Inaddition, digital images can be output in a data format to a storagemedium 23 such as a floppy disk for displaying later at a remote site.Any digital display device may be used, such a common computer printer,X-Y plotter, or a display screen.

An example of the output of the scanner 20 to the computer 12 is adigital image 24 shown in FIG. 2. More accurately, the scanner outputsdata representative of the digital image and the computer causes thedigital image 24 to be displayed on the display monitor 14. As usedherein “digital image” refers to the digital data representative of thedigital image, the digital image displayed on the monitor or otherdisplay screen, and the digital image printed by the printer 22 or aremote printer.

The digital image 24 is depicted using numerous pixels 24 having variouspixel values. In the gray-scale image 24 the pixel values are luminancevalues representing a brightness level varying from black to white. In acolor image the pixels have color values and luminance values, both ofwhich being pixel values. The color values can include the values of anycomponents in a representation of the color by a vector. FIG. 3 showsdigital image 24A in the form of an array of pixels 26. Each pixel isassociated with one or more pixel values, which in the example shown inFIG. 3 are luminance values from 0 to 15.

The digital image 24 shown in FIG. 2 includes thousands of pixels. Thedigital image 24A represented in FIG. 3 includes 225 pixels. Theinvention preferably is used for images having pixels numbering in themillions. Therefore, the description herein is necessarily a simplisticdiscussion of the utility of the invention.

According to a preferred embodiment of the invention numerous candidatepoints are located within the original image. Signature points areselected from among the candidate points and are altered to form asignature. The signature is a pattern of any number of signature points.In a preferred embodiment, the signature is a binary number between 16and 32 bits in length. The signature points may be anywhere within animage, but are preferably chosen to be as inconspicuous as possible.Preferably, the number of signature points is much greater than thenumber of bits in a signature. This allows the signature to beredundantly encoded in the image. Using a 16 to 32 bit signature, 50-200signature points are preferable to obtain multiple signatures for theimage.

A preferred embodiment of the invention locates candidate points byfinding relative maxima and minima, collectively referred to as extrema,in the image. The extrema represent local extremes of luminance orcolor. FIG. 4 shows what is meant by relative extrema. FIG. 4 is agraphical representation of the pixel values of a small portion of adigital image. The vertical axis of the graph shows pixel values whilethe horizontal axis shows pixel positions along a single line of thedigital image. Small undulations in pixel values, indicated at 32,represent portions of the digital image where only small changes inluminance or color occur between pixels. A relative maximum 34represents a pixel that has the highest pixel value for a given area ofthe image. Similarly, a relative minimum 36 represents a pixel that hasthe lowest pixel value for a given area of the image.

Relative extrema are preferred signature points for two major reasons.First, they are easily located by simple, well known processing. Second,they allow signature points to be encoded very inconspicuously.

One of the simplest methods to determine relative extrema is to use a“Difference of Averages” technique. This technique employs predeterminedneighborhoods around each pixel 26; a small neighborhood 28 and a largeneighborhood 30, as shown in FIGS. 2 and 3. In the present example theneighborhoods are square for simplicity, but a preferred embodimentemploys circular neighborhoods. The technique determines the differencebetween the average pixel value in the small neighborhood and theaverage pixel value of the large neighborhood. If the difference islarge compared to the difference for surrounding pixels then the firstpixel value is a relative maxima or minima.

Using the image of FIG. 3 as an example, the Difference of Averages forthe pixel 26A is determines as follows. The pixel values within the 3×3pixel small neighborhood 28A add up to 69; dividing by 9 pixels gives anaverage of 7.67. The pixel values within the 5×5 pixel largeneighborhood 30A add up to 219; dividing by 25 pixels gives an averageof 8.76 and a Difference of Averages of −1.09. Similarly, the average insmall neighborhood 28G is 10.0; the average in large neighborhood 30G is9.8; the Difference of Averages for pixel 26G is therefore 0.2. Similarcomputations on pixels 26B-26F produce the following table:

26A 26B 26C 26D 26E 26F 26G Small Neighborhood 7.67 10.56 12.89 14.1113.11 11.56 10.0 Large Neighborhood 8.76 10.56 12.0 12.52 12.52 11.3698.0 Difference of −1.09 0.0 0.89 1.59 0.59 0.2 0.2 Averages

Based on pixels 26A-26G, there may be a relative maximum at pixel 26D,whose Difference of Averages of 1.59 is greater than the Difference ofAverages for the other examined pixels in the row. To determine whetherpixel 26D is a relative maximum rather than merely a small undulation,its Difference of Averages must be compared with the Difference ofAverages for the pixels surrounding it in a larger area.

Preferably, extrema within 10% of the image size of any side are notused as signature points. This protects against loss of signature pointscaused by the practice of cropping the border area of an image. It isalso preferable that relative extrema that are randomly and widely spaceare used rather than those that appear in regular patterns.

Using the Difference of Averages technique or other known techniques, alarge number of extrema are obtained, the number depending on the pixeldensity and contrast of the image. Of the total number of extrema found,a preferred embodiment chooses 50 to 200 signature points. This may bedone manually by a user choosing with the keyboard 16, mouse 18, orother pointing device each signature point from among the extremadisplayed on the display monitor 14. The extrema may be displayed as adigital image with each point chosen by using the mouse or otherpointing device to point to a pixel or they may be displayed as a listof coordinates which are chosen by keyboard, mouse, or other pointingdevice. Alternatively, the computer 12 can be programmed to choosesignature points randomly or according to a preprogrammed pattern.

One bit of binary data is encoded in each signature point in the imageby adjusting the pixel values at and surrounding the point. The image ismodified by making a small, preferably 2%-10% positive or negativeadjustment in the pixel value at the exact signature point, to representa binary zero or one. The pixels surrounding each signature point, inapproximately a 5×5 to 10×10 grid, are preferably adjustedproportionally to ensure a continuous transition to the new value at thesignature point. A number of bits are encoded in the signature points toform a pattern which is the signature for the image.

In a preferred embodiment, the signature is a pattern of all of thesignature points. When auditing a subject image, if a statisticallysignificant number of potential signature points in the subject imagematch corresponding signature points in the signed image, then thesubject image is deemed to be derived from the signed image. Astatistically significant number is somewhat less than 100%, but enoughto be reasonably confident that the subject image was derived from thesigned image.

In an alternate embodiment, the signature is encoded using a redundantpattern that distributes it among the signature points in a manner thatcan be reliably retrieved using only a subset of the points. Oneembodiment simply encodes a predetermined number of exact duplicates ofthe signature. Other redundant representation methods, such as anerror-correcting code, may also be used.

In order to allow future auditing of images to determine whether theymatch the signed image, the signature is stored in a database in whichit is associated with the original image. The signature can be stored byassociating the bit value of each signature point together with x-ycoordinates of the signature point. The signature may be storedseparately or as part of the signed image. The signed image is thendistributed in digital form.

As discussed above, the signed image may be transformed and manipulatedto form a derived image. The derived image is derived from the signedimage by various transformations, such as resizing, rotating, adjustingcolor, brightness and/or contrast, cropping and converting to print orfilm. The derivation may take place in multiple steps or processes ormay simply be the copying of the signed image directly.

It is assumed that derivations of these images that an owner wishes totrack include only applications which substantially preserve theresolution and general quality of the image. While a size reduction by90%, a significant color alteration or distinct-pixel-value reductionmay destroy the signature, they also reduce the image's significance andvalue such that no auditing is desired.

In order to audit a subject image according to a preferred embodiment, auser identifies the original image of which the subject image issuspected of being a duplicate. For a print or film image, the subjectimage is scanned to create a digital image file. For a digital image, noscanning is necessary. The subject digital image is normalized usingtechniques as described below to the same size, and same overallbrightness, contrast and color profile as the unmodified original image.The subject image is analyzed by the method described below to extractthe signature, if present, and compare it to any signatures stored forthat image.

The normalization process involves a sequence of steps to undotransformations previously made to the subject image, to return it asclose as possible to the resolution and appearance of the originalimage. It is assumed that the subject image has been manipulated andtransformed as described above. To align the subject image with theoriginal image, a preferred embodiment chooses three or more points fromthe subject image which correspond to points in the original image. Thethree or more points of the subject image are aligned with thecorresponding points in the original image. The points of the subjectimage not selected are rotated and resized as necessary to accommodatethe alignment of the points selected.

For example, FIG. 5 shows a digital subject image 38 that is smallerthan the original image 24 shown in FIG. 2. To resize the subject image,a user points to three points such as the mouth 40B, ear 42B and eye 44Bof the subject image using the mouse 18 or other pointer. Since it isusually difficult to accurately point to a single pixel, the computerselects the nearest extrema to the pixel pointed to by the user. Theuser points to the mouth 40A, ear 42A, and eye 44A of the originalimage. The computer 12 resizes and rotates the subject image asnecessary to ensure that points 40B, 42B, and 44B are positioned withrespect to each other in the same way that points 40A, 42A, and 44A arepositioned with respect to each other in the original image. Theremaining pixels are repositioned in proportion to the repositioning ofpoints 40B, 42B and 44B. By aligning three points the entire subjectimage is aligned with the original image without having to align eachpixel independently.

After the subject image is aligned, the next step is to normalize thebrightness, contrast and/or color of the subject image. Normalizinginvolves adjusting pixel values of the subject image to match thevalue-distribution profile of the original image. This is accomplishedby a technique analogous to that used to align the subject image. Asubset of the pixels in the subject image are adjusted to equalcorresponding pixels in the original image. The pixels not in the subsetare adjusted in proportion to the adjustments made to the pixels in thesubset. The pixels of the subject image corresponding to the signaturepoints should not be among the pixels in the subset. Otherwise anysignature points in the subject image will be hidden from detection whenthey are adjusted to equal corresponding pixels in the original image.

In a preferred embodiment, the subset includes the brightest and darkestpixels of the subject image. These pixels are adjusted to have luminancevalues equal to the luminance values of corresponding pixels in theoriginal image. To ensure that any signature points can be detected, nosignature points should be selected during the signature embeddingprocess described above that are among the brightest and darkest pixelsof the original image. For example, one could use pixels among thebrightest and darkest 3% for the adjusting subset, after selectingsignature points among less than the brightest and darkest 5% to ensurethat there is no overlap.

When the subject image is fully normalized, it is preferably compared tothe original image. One way to compare images is to subtract one imagefrom the other. The result of the subtraction is a digital image thatincludes any signature points that were present in the subject image.These signature points, if any, are compared to the stored signaturepoints for the signed image. If the signature points do not match, thenthe subject image is not an image derived from the signed image, unlessthe subject image was changed substantially from the signed image.

In an alternative embodiment, the normalized subject image is compareddirectly with the signed image instead of subtracting the subject imagefrom the original image. This comparison involves subtracting thesubject image from the signed image. If there is little or no imageresulting from the subtraction, then the subject image equals to thesigned image, and therefore has been derived from the signed image.

In another alternate embodiment, instead of normalizing the entiresubject image, only a section of the subject image surrounding eachpotential signature point is normalized to be of the same generalresolution and appearance as a corresponding section of the originalimage. This is accomplished by selecting each potential signature pointof the subject image and selecting sections surrounding each potentialsignature point. The normalization of each selected section proceedsaccording to methods similar to those disclosed above for normalizingthe entire subject image.

Normalizing each selected section individually allows each potentialsignature point of the subject image to be compared directly with acorresponding signature point of the signed image. Preferably, anaverage is computed for each potential signature point by averaging thepixel value of the potential signature point with the pixel values of aplurality of pixels surrounding the potential signature point. Theaverage computed for each signature is compared directly with acorresponding signature point of the signed image.

While the methods of normalizing and extracting a signature from asubject image as described above are directed to luminance values,similar methods may be used for color values. Instead of or in additionto normalizing by altering luminance values, the color values of thesubject image can also be adjusted to equal corresponding color valuesin an original color image. However, it is not necessary to adjust colorvalues in order to encode a signature in or extract a signature from acolor image. Color images use pixels have pixel values that includeluminance values and color values. A digital signature can be encoded inany pixel values regardless of whether the pixel values are luminancevalues, color values, or any other type of pixel values. Luminancevalues are preferred because alterations may be made more easily toluminance values without the alterations being visible to the human eye.

From the foregoing it will be appreciated that, although specificembodiments of the invention have been described herein for purposes ofillustration, various modifications may be made without deviating fromthe spirit and scope of the invention. Accordingly, the invention is notlimited except as by the appended claims.

What is claimed is:
 1. A method of marking a work of authorship without apparent evidence of data alteration, the work being represented by a set of data elements, the method comprising: providing first plural-bit data; computing from the first plural-bit data additional, error correcting data corresponding thereto; and altering the values of at least certain of said data elements in accordance with composite data comprising the first data and the error correcting data, including increasing the value of a first data element, decreasing the value of a second data element, increasing the values of other data elements adjoining the first data element, and reducing the values of other data elements adjoining the second data element.
 2. A method of marking a work of authorship without apparent evidence of data alteration, the work being represented by a set of data elements, the method comprising providing first plural-bit data, computing from the first plural-bit data additional, error correcting data corresponding thereto; and altering the values of at least certain of said data elements in accordance with composite data comprising the first data and the error correcting data, including altering said certain data elements by an amount based, at least in part, on initial values thereof.
 3. The method of claim 2 in which said amount is ten percent or less.
 4. A method of marking a work of authorship without apparent evidence of data alteration, the work being represented by a set of data elements, the method comprising providing first plural-bit data, computing from the first plural-bit data additional, error correcting data corresponding thereto; and altering the values of at least certain of said data elements in accordance with composite data comprising the first data and the error correcting data; wherein at least certain excerpts of the work are not marked because marking in such excerpts would more likely be conspicuous. 